EMDS-7: Environmental Microorganism Image Dataset Seventh Version for Multiple Object Detection Evaluation
This provides a specialized dataset for researchers in microbiology and computer vision to benchmark object detection models, but it is incremental as a new version of an existing dataset.
The paper introduces EMDS-7, a dataset of 2365 microscopic images with 13216 labeled objects across 41 types of environmental microorganisms, designed for object detection evaluation, and validates it using common deep learning methods like Faster-RCNN and YOLOv3.
The Environmental Microorganism Image Dataset Seventh Version (EMDS-7) is a microscopic image data set, including the original Environmental Microorganism images (EMs) and the corresponding object labeling files in ".XML" format file. The EMDS-7 data set consists of 41 types of EMs, which has a total of 2365 images and 13216 labeled objects. The EMDS-7 database mainly focuses on the object detection. In order to prove the effectiveness of EMDS-7, we select the most commonly used deep learning methods (Faster-RCNN, YOLOv3, YOLOv4, SSD and RetinaNet) and evaluation indices for testing and evaluation. EMDS-7 is freely published for non-commercial purpose at: https://figshare.com/articles/dataset/EMDS-7_DataSet/16869571